scholarly journals Operator for Construction of Archimedian Triangular Norms

2019 ◽  
pp. 208-210 ◽  
Author(s):  
Roman Vorobel
Keyword(s):  
Fuzzy Logic ◽  
Boundary Conditions ◽  
Fuzzy Systems ◽  
Triangular Norms ◽  
Associative Functions

Triangular norms and associative functions arebase of connectives in fuzzy logic and fuzzy systems. Newconnective operator that can generate different classes of fuzzyconnectives is proposed. It is proved that this operator satisfiesthe requirements of such axioms as commutativity, associativity,monotonicity and boundary conditions. It is parameterized andtherefore new triangular norms are obtained. Constructedparameterized triangular norms are of a strict and Archimediantype.

The Use of Fuzzy Logic in Shipping and Shipbuilding Market Modeling, Analysis, and Forecasting

1996 ◽  
Vol 12 (02) ◽  
pp. 85-98
Author(s):  
Jun Li ◽  
Michael G. Parsons
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Systems ◽  
Freight Rate ◽  
New Order ◽  
Market Mechanisms ◽  
Promising Alternative ◽  
Market Participants ◽  
Model Free ◽  
Modeling Analysis ◽  
Oil Tanker

Fuzzy logic is a technique that attempts to systematically and mathematically emulate human reasoning. This paper investigates the feasibility of applying fuzzy logic to transportation and shipbuilding market modeling, analysis and forecasting. Fuzzy systems called fuzzy decision modelers (FDMs) are developed based on fuzzy logic techniques to model the crude oil tanker freight rate market, the tanker new order market and the tanker scrapping market. Our results show that the FDMs are able to model and forecast these economic systems very well. In addition, the FDMs also provide valuable insights into market mechanisms and market participants' decision-making patterns. The FDMs are mathematical model-free, nonlinear systems capable of capturing complicated relationships among economic variables. The FDMs are easy to develop and easy to interpret. These advantages of fuzzy systems suggest that fuzzy logic techniques are a promising alternative in shipping and shipbuilding market modeling, analysis and forecasting.

scholarly journals ANALISIS RULE INFERENSI FUZZY MAMDANI DALAM MENENTUKAN IPK AKHIR

2017 ◽  
Vol 1 (1) ◽  
pp. 22
Author(s):  
Khairul Saleh
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Mathematical Models ◽  
Dynamic Systems ◽  
Fuzzy Systems ◽  
Chaotic Dynamics ◽  
Complex Dynamic ◽  
Grade Point ◽  
Complex Dynamic Systems ◽  
Decision Making System

Abstract - In the world of education to achieve the level of success, of course, they have a benchmark for the success of students, one of them is the Grade Point Average (GPA). The purpose of this study is to determine the final GPA so that later it can be used as a reference to predict the success rate of students. The issue of decision-making systems using Fuzzy systems is very suitable for definite reasoning or estimation, especially for systems with strict mathematical models that are difficult to get a definite decision. Fuzzy logic can be used to describe a system of chaotic dynamics, and fuzzy logic can be useful for complex dynamic systems where solutions to common mathematical models cannot work well. The Mamdani method computes efficiently and works well with optimization and adaptive techniques, which makes it very good in control problems, especially for dynamic non-linear systems. Keywords - Cumulative Achievement Index (GPA), fuzzy system, decision making system, mamdani information

Computational Intelligence for Modelling and Control of Multi-Robot Systems

2011 ◽  
pp. 122-135
Author(s):  
M. Mohammadian
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Control Systems ◽  
Fuzzy Systems ◽  
Methodological Approach ◽  
Knowledge Bases ◽  
Fuzzy Logic Systems ◽  
Fuzzy Knowledge Bases ◽  
Robot Systems ◽  
Logic Systems

With increased application of fuzzy logic in complex control systems, there is a need for a structured methodological approach in the development of fuzzy logic systems. Current fuzzy logic systems are developed based on individualistic bases and cannot face the challenge of interacting with other (fuzzy) systems in a dynamic environment. In this chapter a method for development of fuzzy systems that can interact with other (fuzzy) systems is proposed. Specifically a method for designing hierarchical self-learning fuzzy logic control systems based on the integration of genetic algorithms and fuzzy logic to provide an integrated knowledge base for intelligent control of mobile robots for collision-avoidance in a common workspace. The robots are considered as point masses moving in a common work space. Genetic algorithms are employed as an adaptive method for learning the fuzzy rules of the control systems as well as learning, the mapping and interaction between fuzzy knowledge bases of different fuzzy logic systems.

scholarly journals Fuzzy Dynamic Adaptation of Gap Generation and Mutation in Genetic Optimization of Type 2 Fuzzy Controllers

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Leticia Cervantes ◽  
Oscar Castillo ◽  
Denisse Hidalgo ◽  
Ricardo Martinez-Soto
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Flight Control ◽  
Fuzzy Systems ◽  
Performance Enhancement ◽  
Fuzzy Controller ◽  
Control Problems ◽  
Genetic Optimization ◽  
Two Parameters

We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.

REVISITING Xor-IMPLICATIONS: CLASSES OF FUZZY (CO)IMPLICATIONS BASED ON f-Xor (f-XNor) CONNECTIVES

2013 ◽  
Vol 21 (06) ◽  
pp. 899-925 ◽  
Author(s):  
BENJAMÍN BEDREGAL ◽  
RENATA HAX SANDER REISER ◽  
GRAÇALIZ PEREIRA DIMURO
Keyword(s):  
Boundary Conditions ◽  
Unit Interval ◽  
End Points ◽  
Triangular Norms ◽  
Fuzzy Implications ◽  
Classical Definition ◽  
Definition Of ◽  
Exclusive Or ◽  
Intrinsic Definition ◽  
Fuzzy Connectives

The main contribution of this paper is the introduction of an intrinsic definition of the connective “fuzzy exclusive or” E (f-Xor E), based only on the properties of boundary conditions, commutativity and partial isotonicity-antitonicity on the the end-points of the unit interval U = [0,1], in a way that the classical definition of the boolean Xor is preserved. We show three classes of the f-Xor E that can be also obtained from the composition of fuzzy connectives, namely, triangular norms, triangular conorms and fuzzy negations. A discussion about extra properties satisfied by the f-Xor E is presented. Additionally, the paper introduces a class of fuzzy equivalences that generalizes the Fodor and Roubens's fuzzy equivalence, and four classes of fuzzy implications induced by the f-Xor E, discussing their main properties. The relationships between those classes of fuzzy implications and automorphisms are explored. The action of automorphisms on f-Xor E is analyzed.

scholarly journals GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2818
Author(s):  
Pedro J. Correa-Caicedo ◽  
Horacio Rostro-González ◽  
Martin A. Rodriguez-Licea ◽  
Óscar Octavio Gutiérrez-Frías ◽  
Carlos Alonso Herrera-Ramírez ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Embedded System ◽  
Fuzzy Systems ◽  
Intelligent System ◽  
Unscented Kalman Filter ◽  
The Other ◽  
Linear Functions ◽  
Sensor Technology ◽  
Raspberry Pi ◽  
Land Vehicles

GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand.

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